The combination of different losses forms a multi-task learning problem, thus an automatic weighting method proposed firstly by Kendal  [1]} and investigated in remote sensing in [2]}, [3]} is applied to automatically tune the loss weights considering the homoscedastic uncertainty of each separate task:
\(w_{l}=\left\lbrace \begin{array}{ll}0.5 \cdot \exp \left(-\log \left(\sigma _{l}^{2}\right)\right) & \text{ for } \mathcal {L}_{L_{1}} \text{ and } \mathcal {L}_{\text{normal }} \\\exp \left(-\log \left(\sigma _{l}^{2}\right)\right) & \text{ for } \mathcal {L}_{\text{cLSGAN }}\end{array}\right.\) 
